This vignette demonstrates analysing RNA Velocity quantifications stored in a Seurat object. If you use scVelo in your work, please cite:

Prerequisites to install:

library(Seurat)
library(SeuratDisk)
library(SeuratWrappers)
# If you don't have velocyto's example mouse bone marrow dataset, download with the CURL command
# curl::curl_download(url = 'http://pklab.med.harvard.edu/velocyto/mouseBM/SCG71.loom', destfile
# = '~/Downloads/SCG71.loom')
ldat <- ReadVelocity(file = "~/Downloads/SCG71.loom")
bm <- as.Seurat(x = ldat)
bm[["RNA"]] <- bm[["spliced"]]
bm <- SCTransform(bm)
bm <- RunPCA(bm)
bm <- RunUMAP(bm, dims = 1:20)
bm <- FindNeighbors(bm, dims = 1:20)
bm <- FindClusters(bm)
DefaultAssay(bm) <- "RNA"
SaveH5Seurat(bm, filename = "mouseBM.h5Seurat")
Convert("mouseBM.h5Seurat", dest = "h5ad")
# In Python
import scvelo as scv
adata = scv.read("mouseBM.h5ad")
adata
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
scv.tl.velocity(adata)
scv.tl.velocity_graph(adata)
scv.pl.velocity_embedding_stream(adata, basis="umap", color="seurat_clusters")

scv.pl.velocity_embedding(adata, basis="umap", color="seurat_clusters", arrow_length=3, arrow_size=2, dpi=120)

scv.tl.recover_dynamics(adata)
scv.tl.latent_time(adata)
scv.pl.scatter(adata, color="latent_time", color_map="gnuplot")

top_genes = adata.var["fit_likelihood"].sort_values(ascending=False).index[:300]
scv.pl.heatmap(adata, var_names=top_genes, sortby="latent_time", col_color="seurat_clusters", n_convolve=100)